Gaussian Processes for Advanced Motion Control

Maurice Poot*, Jim Portegies, Noud Mooren, Max van Haren, Max van Meer, Tom Oomen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
8 Downloads (Pure)


Machine learning techniques, including Gaussian processes (GPs), are expected to play a significant role in meeting speed, accuracy, and functionality requirements in future data-intensive mechatronic systems. This paper aims to reveal the potential of GPs for motion control applications. Successful applications of GPs for feedforward and learning control, including the identification and learning for noncausal feedforward, position-dependent snap feedforward, nonlinear feedforward, and GP-based spatial repetitive control, are outlined. Experimental results on various systems, including a desktop printer, wirebonder, and substrate carrier, confirmed that data-based learning using GPs can significantly improve the accuracy of mechatronic systems.

Original languageEnglish
Pages (from-to)396-407
JournalIEEJ Journal of Industry Applications
Issue number3
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • feedforward control
  • gaussian processes
  • learning control


Dive into the research topics of 'Gaussian Processes for Advanced Motion Control'. Together they form a unique fingerprint.

Cite this